The goal of this project is to develop improved statistical methods for the analysis of data from human studies. In disease screening studies, it is difficult to obtain direct information on the age at onset of disease, the timing of past exposures, and the rate of disease progression. Work has progressed in the development of methods for disease screening studies, motivated by the NIEHS uterine fibroid (leiomyoma) study conducted by Donna Baird. We developed a method for estimating age-specific cumulative incidence using data on current status of disease at a single screening examination along with each individual's clinical history. Based on applying our method, African American women appear to have significantly higher rates of uterine leiomyoma than White women. We also developed a Bayesian approach that incorporates current disease severity information in estimating incidence and rates of disease progression. Based on this approach, we found African American woman to have not only higher incidence rates but also higher rates of progression of leiomyoma. Finally, we developed a general model for assessing beneficial effects of time-dependent covariates on delaying disease onset and curing preexisting disease. This model was motivated by tumor biology, and was applied to assess the effects of childbirth on uterine leiomyoma.